Exploratory analysis of user-generated photos and indicators that influence their appeal

In this paper we analyze if simple indicators related to photo quality (brightness, sharpness, color palette) and established content detection techniques (face detection) can predict the success of photos in obtaining more “likes” from other users of photo-sharing social networks. This provides a unique look into the habits of users of such networks. The analysis was performed on 394.000 images downloaded from the social photo-sharing site Instagram, paired with a de-identified dataset of user liking activity, provided by a seller of a social-media mobile app. Two user groups were analyzed: all users in a two month period (N = 122.260) and a highly selective group (N = 3.982) of users that only like <10% of what they view. No correlation was found with any of the indicators using the whole (non-selective) population, likely due to their bias towards earning virtual currency in exchange for liking. However, in selective group, small positive correlation was found between like ratio and image sharpness (r=0.09, p<0.0001) and small negative correlation between like ratio and the number of faces (r=-0.10, p<0.0001)